Refining discordant gene trees
 Pawel Górecki^{1}Email author and
 Oliver Eulenstein^{2}
https://doi.org/10.1186/1471210515S13S3
© Górecki and Eulenstein; licensee BioMed Central Ltd. 2014
Published: 13 November 2014
Abstract
Background
Evolutionary studies are complicated by discordance between gene trees and the species tree in which they evolved. Dealing with discordant trees often relies on comparison costs between gene and species trees, including the wellestablished RobinsonFoulds, gene duplication, and deep coalescence costs. While these costs have provided credible results for binary rooted gene trees, corresponding cost definitions for nonbinary unrooted gene trees, which are frequently occurring in practice, are challenged by biological realism.
Result
We propose a natural extension of the wellestablished costs for comparing unrooted and nonbinary gene trees with rooted binary species trees using a binary refinement model. For the duplication cost we describe an efficient algorithm that is based on a linear time reduction and also computes an optimal rooted binary refinement of the given gene tree. Finally, we show that similar reductions lead to solutions for computing the deep coalescence and the RobinsonFoulds costs.
Conclusion
Our binary refinement of RobinsonFoulds, gene duplication, and deep coalescence costs for unrooted and nonbinary gene trees together with the linear time reductions provided here for computing these costs significantly extends the range of trees that can be incorporated into approaches dealing with discordance.
Keywords
Introduction
Gene trees represent estimates of evolutionary histories of gene families, and are fundamental for evolutionary biological research [1, 2]. Often gene trees are assumed to reflect the evolutionary history of species, or species tree, from which their sequences were sampled, presenting a common approach of species tree inference [3–7]. Gene trees can also provide fundamental information to study the evolution of biochemical function in gene families [8].
Gene trees can be inferred from multiple sequence alignments of sequences culled from a gene family. The number of these sequences as well as their evolutionary complexity has expanded on an unprecedented scale in recent years [9], prompting the estimation of ever larger and more credible gene trees. Despite these potentials, evolutionary biologists have long recognized the potential for substantial discordance among the gene trees as well as among the gene trees and the species tree in which they evolve [10–14], challenging traditional phylogenetic gene tree and species tree estimation. Discordance can be caused by error as well as major evolutionary processes, such as the duplication of genes or deep coalescence. Complicating matters further such error and evolutionary processes can occur on a staggering scale [15, 16]. For example simulations with realistic parameters suggested that analyzes individual avian genes frequently resulted in trees with substantial error [17], and evolutionary processes cause discordance among evolutionary relationships of major avian groups [18]. Consequently, phylogenetic approaches are challenged to deal with error as well as complex histories of evolutionary processes in order to explain discordance in gene trees [19–21].
A common approach to deal with discordance in gene trees is by representing them with an estimate of the species tree that is thought to be the median tree of the gene trees under a particular (topological comparison) cost from a gene tree to a species tree, which is often referred to as a supertree [22]. A median tree S for a given cost and a collection of trees minimizes the sum of the pairwise costs from every gene tree to S. While varies costs have been proposed [23–26], here we are concerned with the wellresearched RobinsonFoulds, duplication, and deep coalescence costs. The RobinsonFoulds cost is measuring quantitative dissimilarities between two trees without relying on an evolutionary model, and is therefore well suited to address discordance caused by error [27, 28]. In difference, the costs for the evolutionary events gene duplication and deep coalescence are both based on an evolutionary parsimony model allowing to resolve discord based on such events [29, 30].
However, the presented costs are not well adapted to biological realism [31, 32]. In practice gene trees are frequently inferred from sequences that do not permit reliable estimations of rootings or bifurcations [33], and therefore are unrooted and nonbinary. The original evolutionary costs for gene duplication and deep coalescence can not be applied to such trees, since they are only defined for rooted and binary gene trees. In contrast the RobinsonFoulds distance is formally defined for unrooted and nonbinary trees, but multifurcations in phylogenetic trees are interpreted as true evolutionary multifurcations (hard multifurcations). However, nonbinary relationships in gene trees represent uncertainties about the correct binary relationships (soft multifurcations), rather than hard multifurcations which are rare [34]. Consequently, all of the the presented costs are not applicable to a large number of gene trees in practice.
More recently, a binary refinement model for the duplication cost [35] and the deep coalescence cost [36, 37] for rooted gene trees that are nonbinary were introduced. Here we propose a natural extension of this model for our costs to compare unrooted and nonbinary gene trees with rooted binary species trees, and describe linear time reductions to compute these costs.
Related work
Here we provide definitions as well as computational and applicability results, first for the RobinsonFoulds cost, and then for the duplication and deep coalescence costs.
The RobinsonFoulds cost is an elementary tool for estimating quantitative dissimilarities between phylogenetic trees [38–40]. This cost is defined for two trees to be the cardinality of the symmetric difference of their split presentations for unrooted trees, and of their cluster presentations for rooted trees. The splitpresentation of an unrooted tree is the set of all bipartitions, called splits, of the trees' taxon set induced by the removal of an edge [39, 41]. Analogously, the cluster presentation of a rooted tree is the set of all taxon sets of its full subtrees [39]. The RobinsonFoulds cost for two trees, both either unrooted or rooted, satisfies the metric properties [38], and can be computed in linear time [42]. A randomized approximation scheme computes, in sublinear time and with high probability, a (1 + ∈) approximation of the RobinsonFoulds cost [43]. More recently, the RobinsonFoulds cost between an unrooted tree and a rooted tree was introduced in [44] to be the minimum cost under all pairs consisting of a rooting of the unrooted tree and the rooted tree. In fact, this cost is still computable in linear time [44]. Moreover, the distribution of the RobinsonFoulds distance relative to a fixed tree can be computed in linear time [45]. Note, the skewed distribution of the RobinsonFoulds metric suggests that it is only of use when the trees to be compared are quite similar [46]. While the RobinsonFoulds cost is widespread for the comparative analysis of phylogenetic trees, it does not rely on a biological model explaining the difference between trees. Therefore, the RobinsonFoulds cost is generally applicable to any type of trees, e.g. linguistic trees [47] and trees representing dominance hierarchies [48].
In contrast, the duplication and the deep coalescence costs rely on a biological model explaining the discordance between a gene tree and a species tree based on evolutionary events. For a gene and a species tree, both rooted and binary, the duplication cost and the deep coalescence cost are defined to be the minimum number of gene duplications and coalescences, respectively, required to reconcile the gene tree with the species tree [49, 50]. While theses costs are not symmetric, they are computable in linear time [51, 52], and allow to infer credible species trees [53–57]. Furthermore, gene trees that are reconciled by the minimum number of evolutionary events allow studying complex histories of evolutionary events [54, 58]. The gene duplication and deep coalescence costs can also be defined for binary unrooted gene trees and binary rooted species trees as the minimum cost under all rootings of the gene tree and computed in linear time [32, 59, 60]. However, often gene trees are unrooted and nonbinary in practice. While existing definitions for such gene trees and rooted binary species trees are linear time computable [31, 32], they are not well adapted to biological realism. More recently, cost definitions for such trees were introduced that are based on a binary refinement model, by choosing the minimum cost between every binary refinement of a rooted gene tree and a rooted binary species tree, which are polynomial time computable [35, 61]. In contrast, finding the minimum cost between a rooted binary gene tree and all binary refinements of a rooted nonbinary species tree is NP hard [37]. However, costs under a binary refinement model for unrooted and nonbinary gene trees have not been addressed in the literature. For a detailed overview about gene tree reconciliation the interested reader is referred to [62].
Contributions
Here, we define the RobinsonFoulds, duplication, and deep coalescence costs for unrooted and nonbinary gene trees and a rooted binary species tree under the binary refinement model. To compute the duplication cost we describe a linear time reduction from the problem of computing optimal binary refinements of unrooted gene trees to the problem of computing such refinements for rooted gene trees. The latter problem can be solved in linear time [37]. Then, based on the theory of unrooted tree reconciliation [32, 44, 63, 59], we prove that the duplication cost has similar properties to the deep coalescence and RobinsonFoulds costs when comparing unrooted and nonbinary gene trees with rooted species trees. From this follows that we can prove linear time reductions for the deep coalescence and the RobinsonFoulds costs that are similar to our reduction for the duplication cost. Since our reductions require only linear time, the runtime to compute the optimal binary refinements of unrooted gene trees is bound by the time complexity of computing optimal binary refinement for rooted binary gene trees.
Basic definitions and preliminaries
An unrooted tree T is an acyclic, connected, and undirected graph that has no degreetwo nodes, and every degreeone node is labeled with a species name. The degreeone nodes are called leaves; and the remaining nodes are called internal nodes. A tree is binary if every internal node has degree three. A rooted tree is defined similar to an unrooted tree, with the difference that it has a distinguished node, called root. A contraction of an edge e of an (un)rooted tree T removes e from T and merges both ends of e into a single node. A binary refinement of an unrooted or rooted tree T is a binary tree that can be transformed into T by contractions. By L(T ) we denote the set of all leaf labels in T .
A rooted tree S with a unique leaf labeling is called a species tree. For two nodes a, b of S, a ⊕ b is the least common ancestor of a and b in S. Let T and be a rooted tree (called rooted gene tree) such that L(T ) ⊆ L(S). By M : T → S we denote the least common ancestor (lca) mapping between the nodes of T and S that preserves the labeling of the leaves. The duplication cost between T and S, is defined by: D(T, S) := {M (g) = M (c) : c is a child of an internal node g ∈ T }.
Let G = 〈V_{ G }, E_{ G }〉 be an unrooted tree (called unrooted gene tree). A rooting of G is defined by choosing an edge e from G on which the root is to placed. Such a rooted tree will be denoted by G_{ e }. Note that G_{ e } has one more node (the root) that G. A rooted binary refinement of an unrooted gene tree G, is a binary refinement of a rooting of G.
The edges with minimal cost will be called optimal. In the remainder of this work we show first how to compute urD in linear time and space, and then solve the following problem. Observe, that in contrast to our previous study [44, 32, 64], here, for the first time, we extend the notion of rooting by incorporating rooting at nodes.
Problem 1 For a given unrooted gene tree G and a binary species tree S, find the binary refinement of under all rootings of G that minimizes the duplication cost.
A similar problem for rooted gene trees was solved in [35]. In the remaining section we show how to reduce Problem 1 to the rooted problem in linear time.
Unrooted reconciliation
There is a limited number of star types in gene trees [44]. Let K be a star with center v and k siblings as indicated in Figure 1a. Let α denote the number of edges satisfying a_{ i } = ⊤. Similarly, we define β for bi's. Then, K has type: M1 if α = 1 and β = k − 1 and all edges labeled by ⊤ are connected to the k siblings of v, M2 if α = 0 and β = k − 1, M3 if α = 1 and β = k, M4 if α = β = k, M5 if 1 < α < β = k and M6 if α = 0 and β = k.
Proposition 1 For a given unrooted gene tree G and a species tree S a gene tree G can have any number of stars M1. For the remaining stars we have three mutually exclusive cases: (i) G has an empty edge, (ii) G has a double edge or (iii) G has only single edges.
Proof The proof follows easily from the properties of stars. See also Lemma 2 from [44]. □
Observe that in case (i) G has one or two stars M2, in case (ii) G has a star of type M3M5 and in (iii) G has exactly one star of type M6.
The next propositions states a crucial difference between binary and general trees. For the proof please refer to [44].
Proposition 2 If both an unrooted gene tree G and a species S are binary then G has at least one empty or double edge.
Results
Polytomies and the duplication cost
The next two proposition shows how the cost changes when we move a position of the root in G.
Proposition 3 Under the notation from Figure 1. If for some i ∈ {1, 2, . . . , k} one of the following conditions are true:

If the star type is M1 or M3 and b_{ i }= ⊤.

If the star type is M2 and a_{ i } ≠ ⊤ ≠ b_{ i }.
then for every j = 1, 2, . . . , k.
In the case of M2, we have M_{ i }(w_{ i }) ≠ ⊤ ≠ M_{ i }(v) and M_{ i }(w_{ i }) ⊕ M_{ i }(v) = ⊤, thus the root of G_{ i } is a nonduplication node. On the other hand, M_{ j }(v) = ⊤ and the root of G_{ j } is a duplication node. We conclude $c\le D\left({G}_{{e}_{i}},S\right)\le c+1\le D\left({G}_{{e}_{j}},S\right)$. □
Proposition 4 Using the notation from Proposition 3. If the star type is M 4 − M 6 then for all i and j.
Proof Similarly to the proof of the previous proposition, it is easy to show that the root of G_{ ei } is is a duplication node while v is a duplication node, if and only if, the star is of type M4 or M5. Therefore, for every i, $D\left({G}_{{e}_{i}},S\right)=c+1$ if the star type is M6 and $D\left({G}_{{e}_{i}},S\right)=c+2$. Otherwise, where c is defined in the proof of Proposition 3. □
We conclude from Propositions 14:
Theorem 1 For an unrooted gene tree G and a species tree S. If e is an edge of G that is either empty, double or an element of a star M6, then e is optimal.
This observation leads to a linear time and space reduction for urD computation similar to algorithms from [32, 44]. Now we reduce Problem 1, to the problem where gene trees are rooted. In the special case of star M6, we need to root a tree at a node instead of edge. For a nonleaf node v ∈ VG by Gv we denote the tree rooted at v. We refer to the algorithm for refining rooted gene trees from [65] by Bin(T, S), where T is a rooted tree and S is a binary species tree. It is known that Bin(T, S) runs in O(T S) time [65].
Theorem 2 Algorithm 1 infers a rooted binary refinement G∗ of an unrooted gene tree G such that D(G∗, S) = min {urD(G', S) : G' is a binary refinement of G}.
In summary, it is sufficient to identify an optimal edge in G, and then proceed accordingly with the refinement procedure. In steps 35 the algorithm is evaluating labels of edges from $\hat{G}$. The optimal edge is found in the loop present in steps 67. Finally, the refinement procedure is called in steps 910 depending on the type of the star. □
Theorem 3 Algorithm 1 requires O(GS) time, while the reduction (steps 17) can be completed in O(G + S) time and space.
Proof As desired, the result follows from [44] and [37].
Algorithm 1 Resolving polytomies in unrooted gene trees
1: Input A binary species tree S, an unrooted gene tree G with at least three leaves L(G) ⊆ L(S).
2: Output The rooted binary refinement of G with the minimal duplication cost.
3: Let m_{ x },y be the label (a node from S) of 〈x, y〉 in $\hat{G}$. // can be computed in O(G) steps [44].
4: Let v be a node from VG.
5: Let ⊤ := m_{ v },_{ w } ⊕ m_{ w,v } for some edge 〈v, w〉 in G.
6: While there exists a node w adjacent with v such that mw,v = ⊤ ≠ I= m_{ v,w }
7: do: set v := w (star M1).
8: f v is incident with a empty/double edge 〈v, w〉, that is, m_{ v,w } = ⊤ = m_{ w,v } or m_{ v,w } ≠ ⊤≠ m_{ w,v }
9: then return Bin(G_{〈v,w〉}, S) (optimal edge found in star M2M5)
10: else return Bin(G_{ v }, S) (v is the center of star M6).
Examples of (unrooted) binary refinements with costs of all rootings of an unrooted gene tree with multifurcations are depicted in Figures 3, 4 and 5.
Polytomies and other cost functions
Similarly to the gene duplication cost we show results for other cost functions that are related to the duplication cost [63]. Here, we introduce for the first time a general approach, similar to [32, 44], for the case where both trees, i.e., a gene tree and a species tree can be nonbinary.
where T is a rooted gene tree and S is a species tree such that L(T ) ⊆ L(S), I(T ) is the set of all internal nodes of T , K is a cost name and ξ_{ K } : I(T ) → R is a contribution function that for an internal node v of T defines a contribution of v to the cost K when comparing T and S. For a node v in a rooted tree, by c(v) we denote the cluster of v defined as the set of all leaf labels visible from v. The contribution functions for standard costs are defined as follows. Let g be an internal node of T and M be the lcamapping from T to S.

Gene duplication (D) cost function: ξ_{ D }(g) = 1 if g has a child c such that M (g) =M (c), and ξ_{ D }(g) = 0 otherwise.

Deep coalescence (DC): ${\xi}_{D}\left(g\right)=\sum _{}g\prime $, is a child of _{ g } lM (g), M (g!), where x, y is the number of edges on the shortest path connecting nodes x and y in S.

RobinsonFoulds cost (RF): ξ_{ RF }(g) = 1 if c(g) ≠ c(M (g)) and ξ_{ RF }(g) = 0 otherwise.
Note that the classical RobinsonFoulds distance can be obtained by RF (T, S) = I(S) + 2 ∗ ρRF (T, S) − I(T ). Additionally, we have to assume that for the RF distance T is bijectively labelled by the labels present in L(S). For more details and discussion please refer to [44, 63].
where f : E_{ G } → R is a cost function usually defined for a cost K by f(e) = ρ_{ K } (G_{ e }, S). Assume that f_{ S }(e) = D(G_{ e }, S), then it can be proved that ur D(G, S) = Δ(G, S, f_{ S }).
In the previous section we described the solution to Problem 1 defined for the duplication cost by reducing the unrooted problem to a rooted one in linear time and space. Here, we show that the same kind reduction can by applied for the DC and RF cost functions.
Problem 2 (Unrooted refinement under DC cost) For a given unrooted gene tree G and a binary species tree S, find a binary refinement under all rootings of G that minimizes the DC cost.
Problem 3 (Unrooted refinement under RF cost) For a given unrooted gene tree G and a binary species tree S, find a binary refinement under all rootings of G that minimizes the RF cost.
The result for the DC and the RF cost follows from [32] (Proposition 1 and Proposition 2) and [44] (Proposition 1 and Proposition 2), respectively. We conclude, that the statement from Theorem 1 also holds for the DC and RF cost functions. Therefore, Algorithm 1 can be used for locating an optimal edge or star M6 in an unrooted gene tree with multifurcations. Then after such a rooting is identified, one can apply the solution that removes polytomies from rooted gene trees. Clearly this reduction can be performed in linear time and space for both cost functions.
Problem 4 (Rooted refinement under DC cost) For a given rooted gene tree G and a binary species tree S, find a binary refinement under all rootings of G that minimizes the DC cost.
Problem 5 (Rooted refinement under RF cost) For a given rooted gene tree G and a binary species tree S, find a binary refinement under all rootings of G that minimizes the RF cost.
According to our knowledge Problem 4 and Problem 5 are open, with the exception that Problem 4 can be solved in quadratic time for the case when the gene tree has a bijective leaf labelling [36]. We conjecture that these two problems can be solved in polynomial time similarly to the problem under the duplication cost [35] (see Bin(Ge, S) in Algorithm 1). Our reduction shows that Problem 2 and Problem 3 have the same time complexity as the rooted ones.
Conclusion
To deal with discordance in practice we introduced a binary refinement model for the wellstudied RobinsonFoulds, duplication, and deep coalescence costs. To compute these costs we described novel linear time reductions, from which quadratic time algorithms follow for the duplication cost and for the deep coalescence cost when constrained to bijective labelings. Our binary refinement model together with the efficient algorithms allows the exploitation of the full range of available gene trees. Finally, our algorithms not only compute optimal binary refinement costs efficiently, but also simultaneously root and refine gene trees optimally. However, the time complexity of the RobinsonFoulds cost for unrooted and nonbinary gene trees will depend on the time complexity of computing this cost for rooted nonbinary gene trees, which is unknown to the best knowledge of the authors.
Declarations
Acknowledgements
We would like to thank the two reviewers for their detailed comments that allowed us to improve our paper. Furthermore, we would also like to thank Nadia ElMabrouk for helpful discussions.
Declarations
This work was conducted as a part of the Gene Tree Reconciliation Working Group at the National Institute for Mathematical and Biological Synthesis, sponsored by the U.S. National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Department of Agriculture through NSF Award #EF0832858, with additional support from The University of Tennessee, Knoxville. Partial support was provided to OE by the NSF (#0830012 and #106029), and to PG and OE by NCN #2011/01/B/ST6/02777.
This article has been published as part of BMC Bioinformatics Volume 15 Supplement 13, 2014: Selected articles from the 9th International Symposium on Bioinformatics Research and Applications (ISBRA'13): Bioinformatics. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/15/S13.
Authors’ Affiliations
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